Modern Information Retrieval Chapter 3. Evaluation Saif Rababah

Why System Evaluation? There are many retrieval models/ algorithms/ systems, which one is the best? What is the best component for: Ranking function (dot-product, cosine, …) Term selection (stopword removal, stemming…) Term weighting (TF, TF-IDF,…) How far down the ranked list will a user need to look to find some/all relevant documents? Saif Rababah

Difficulties in Evaluating IR Systems Effectiveness is related to the relevancy of retrieved items. Relevancy is not typically binary but continuous. Even if relevancy is binary, it can be a difficult judgment to make. Relevancy, from a human standpoint, is: Subjective: Depends upon a specific user’s judgment. Situational: Relates to user’s current needs. Cognitive: Depends on human perception and behavior. Dynamic: Changes over time. Saif Rababah

Human Labeled Corpora (Gold Standard) Start with a corpus of documents. Collect a set of queries for this corpus. Have one or more human experts exhaustively label the relevant documents for each query. Typically assumes binary relevance judgments. Requires considerable human effort for large document/query corpora. Saif Rababah

Precision and Recall retrieved not retrieved relevant irrelevant not retrieved but relevant retrieved & irrelevant Not retrieved & irrelevant retrieved not retrieved relevant irrelevant Relevant documents Retrieved documents Entire document collection Saif Rababah

Precision and Recall Precision Recall The ability to retrieve top-ranked documents that are mostly relevant. Recall The ability of the search to find all of the relevant items in the corpus. Saif Rababah

Determining Recall is Difficult Total number of relevant items is sometimes not available: Sample across the database and perform relevance judgment on these items. Apply different retrieval algorithms to the same database for the same query. The aggregate of relevant items is taken as the total relevant set. Saif Rababah

Trade-off between Recall and Precision Returns relevant documents but misses many useful ones too The ideal 1 Precision Returns most relevant documents but includes lots of junk 1 Recall Saif Rababah

Computing Recall/Precision Points For a given query, produce the ranked list of retrievals. Adjusting a threshold on this ranked list produces different sets of retrieved documents, and therefore different recall/precision measures. Mark each document in the ranked list that is relevant according to the gold standard. Compute a recall/precision pair for each position in the ranked list that contains a relevant document. Saif Rababah

Computing Recall/Precision Points: An Example Let total # of relevant docs = 6 Check each new recall point: R=1/6=0.167; P=1/1=1 R=2/6=0.333; P=2/2=1 R=3/6=0.5; P=3/4=0.75 R=4/6=0.667; P=4/6=0.667 Missing one relevant document. Never reach 100% recall R=5/6=0.833; p=5/13=0.38 Saif Rababah

Interpolating a Recall/Precision Curve Interpolate a precision value for each standard recall level: rj {0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0} r0 = 0.0, r1 = 0.1, …, r10=1.0 The interpolated precision at the j-th standard recall level is the maximum known precision at any recall level between the j-th and (j + 1)-th level: Saif Rababah

Interpolating a Recall/Precision Curve: An Example 1.0 0.8 0.6 0.4 0.2 1.0 0.2 0.4 0.6 0.8 Recall Saif Rababah

Average Recall/Precision Curve Typically average performance over a large set of queries. Compute average precision at each standard recall level across all queries. Plot average precision/recall curves to evaluate overall system performance on a document/query corpus. Saif Rababah

Compare Two or More Systems The curve closest to the upper right-hand corner of the graph indicates the best performance Saif Rababah

R- Precision Precision at the R-th position in the ranking of results for a query that has R relevant documents. R = # of relevant docs = 6 R-Precision = 4/6 = 0.67 Saif Rababah

F-Measure One measure of performance that takes into account both recall and precision. Harmonic mean of recall and precision: Compared to arithmetic mean, both need to be high for harmonic mean to be high. Saif Rababah

E Measure (parameterized F Measure) A variant of F measure that allows weighting emphasis on precision over recall: Value of  controls trade-off: b = 1: Equally weight precision and recall (E=F). b > 1: Weight recall more. b < 1: Weight precision more. Saif Rababah

Fallout Rate Problems with both precision and recall: Number of irrelevant documents in the collection is not taken into account. Recall is undefined when there is no relevant document in the collection. Precision is undefined when no document is retrieved. Saif Rababah

Subjective Relevance Measure Novelty Ratio: The proportion of items retrieved and judged relevant by the user and of which they were previously unaware. Ability to find new information on a topic. Coverage Ratio: The proportion of relevant items retrieved out of the total relevant documents known to a user prior to the search. Relevant when the user wants to locate documents which they have seen before (e.g., the budget report for Year 2000). Saif Rababah

Other Factors to Consider User effort: Work required from the user in formulating queries, conducting the search, and screening the output. Response time: Time interval between receipt of a user query and the presentation of system responses. Form of presentation: Influence of search output format on the user’s ability to utilize the retrieved materials. Saif Rababah